中国稀土产业发展财政支持效应及其影响因素

doi:10.18402/resci.2020.08.10

摘要/Abstract

摘要：

研究中国稀土产业发展财政支持效应对于完善稀土产业政策理论、优化财政支持政策体系和实现稀土产业可持续发展具有重要价值。本文以中国稀土上市公司为样本,构建了基于SBM-DEA四阶段分析方法的财政支持效率评价模型和基于面板FGLS估计方法的影响因素模型,测算和分析了2010—2019年中国稀土产业财政支持效率及其影响因素,并区分稀土产业链前后端进行比较研究。结果表明：①中国稀土产业的财政支持效率年平均值为1.313,财政支持对65%的稀土上市公司具有“激励效应”,整体达到了财政支持政策设计目标,但仍对35%的稀土上市公司存在“挤出效应”;②稀土产业链前端和后端的财政支持效率年平均值分别为1.145和1.377,后端的财政支持“激励效应”显著大于前端,财政支持有利于推动稀土产业链向后端延伸;③稀土产业财政支持效率随时间推移整体呈下降趋势,“激励效应”逐渐转为“挤出效应”,专项政策的出台能够在短期内提升财政支持效率,但长期效果有限;④财政支持强度与稀土产业财政支持效率之间呈“倒U型”关系,企业规模对稀土产业财政支持效率具有显著正效应。本文深化了财政支持对中国稀土产业发展效应问题的认识,可为中国稀土产业的差异化财政支持政策制订提供参考。

关键词: 财政支持, 稀土, 产业链, 效率评价, 激励效应, 挤出效应, SBM-DEA四阶段模型, 中国

Abstract:

Research on the effect of government financial support on the development of the rare earth industry in China is of great value for improving the theories of policy study of the rare earth industry, optimizing the policy system of government financial support, and realizing the sustainable development of the industry. Taking listed rare earth companies in China as examples, this study constructed an efficiency evaluation model of government financial support based on slacks-based measure data envelopment analysis (SBM-DEA) and an influencing factor model based on panel feasible generalized least squares (FGLS) estimation method, calculated and analyzed the efficiency and influencing factors of government financial support for China’s rare earth industry from 2010 to 2019, and distinguished between the front-end and the back-end of the rare earth industrial chain to compare and analyze the difference of government financial support effects. The results show that: The annual average value of government financial support efficiency of China’s rare earth industry is 1.313. Government financial support has an “incentive effect” on 65% of the listed rare earth companies, which has achieved the overall goal of government financial support policy. But it still has an “exclusion effect” on 35% of the listed rare earth companies. The annual average values of government financial support efficiency of the front-end and the back-end of the rare earth industrial chain are 1.145 and 1.377, respectively, which means that the “incentive effect” of government financial support on the back-end of the inductrial chain is significantly greater than that on the front-end. Government financial support is conducive to promoting the extension of the rare earth industrial chain to the back end. With the passage of time, the government financial support efficiency of the rare earth industry as a whole shows a downward trend, and the “incentive effect” gradually turns into the “exclusion effect”. The introduction of special policies can improve the efficiency of government financial support in the short term, but the long-term effect is limited. There is an inverted U-shaped relationship between government financial support intensity and government financial support efficiency of the rare earth industry, and at the enterprise scale, a significant positive effect on the government financial support efficiency of the rare earth industry can be observed. This study may deepen the understanding of the effect of government financial support on the development of China’s rare earth industry and provide a reference for the formulation of differentiated government financial support policies for China’s rare earth industry.

Key words: government financial support, rare earth, industrial chain, efficiency evaluation, incentive effect, exclusion effect, SBM-DEA four-stage model, China

董娟, 郑明贵, 钟昌标. 中国稀土产业发展财政支持效应及其影响因素[J]. 资源科学, 2020, 42(8): 1551-1565.

DONG Juan, ZHENG Minggui, ZHONG Changbiao. Effect of government financial support on the development of China’s rare earth industry and influencing factors[J]. Resources Science, 2020, 42(8): 1551-1565.

图/表 7

表1

表2

表3

2010—2019年中国稀土上市公司财政支持效率"

企业		2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	多年均值
前端	北方稀土	1.073	1.000	0.879	0.713	0.878	0.816	0.743	0.721	0.830	0.839	0.849
	广晟有色	1.712	1.606	1.740	1.560	0.840	1.012	0.879	1.094	0.884	1.000	1.233
	厦门钨业	0.830	0.913	0.807	0.738	0.658	0.693	0.671	0.771	1.000	0.511	0.759
	中色股份	1.980	1.494	1.048	1.204	1.204	1.212	1.000	1.244	1.235	1.120	1.274
	五矿稀土			1.034	1.144	1.199	1.000	2.266	1.164	1.383	1.320	1.314
	盛和资源			2.874	2.137	1.689	1.511	1.035	0.806	0.830	0.841	1.465
后端	中科三环	1.164	0.976	0.924	0.936	0.881	0.916	0.920	0.885	0.841	0.796	0.924
	中钢天源	2.202	1.749	2.036	1.413	1.552	1.311	2.092	1.080	0.939	0.817	1.519
	银河磁体	3.410	2.236	2.161	2.589	2.132	2.600	2.566	2.244	2.396	2.030	2.436
	宁波韵升	1.273	1.060	1.069	1.151	1.267	1.238	1.035	1.061	1.080	1.058	1.129
	英洛华	1.973	1.554	1.259	1.253	1.696	1.425	1.175	1.189	1.033	0.982	1.354
	有研新材	1.954	1.444	1.731	1.000	0.652	1.022	1.000	0.949	1.087	1.000	1.184
	正海磁材	1.505	0.984	1.285	1.663	1.458	1.126	1.010	1.279	1.076	1.041	1.243
	科恒股份	2.907	1.389	1.920	3.014	2.705	2.333	1.313	0.968	0.826	0.870	1.824
	北矿科技	5.291	2.783	2.910	6.757	1.000	1.989	1.635	1.171	1.137	0.875	2.555
	领益智造	2.418	1.987	1.599	1.442	1.206	0.938	1.077	0.930	1.056	1.000	1.365
	横店东磁	1.213	0.874	0.891	0.859	0.882	0.904	0.892	0.848	0.825	0.709	0.890
	安泰科技	0.786	0.706	0.667	0.807	0.795	0.839	0.807	0.752	0.715	0.735	0.761
	科力远	1.202	0.910	1.189	1.257	1.147	0.581	0.983	0.260	0.867	0.879	0.927
	鸿达兴业				1.150	1.030	1.496	0.747	0.702	0.702	0.734	0.937
均值	全行业	1.935	1.392	1.475	1.639	1.244	1.248	1.192	1.006	1.037	0.958	1.313
	前端	1.399	1.254	1.397	1.250	1.078	1.041	1.099	0.967	1.027	0.939	1.145
	后端	2.100	1.435	1.511	1.806	1.315	1.337	1.232	1.023	1.041	0.966	1.377

表3

图1

表4

表5

表6

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变量类型	变量符号	变量度量	单位
投入变量	x₁	固定资产净值	亿元
	x₂	主营业务成本	亿元
	x₃	员工总人数	人
	x₄	研发支出总额	亿元
产出变量	y	营业收入	亿元
环境变量	W	计入当期损益的政府补助	亿元
影响因素	Sub	政府补助/营业收入	%
影响因素	Size	资产总额的自然对数	—
控制变量	Roe	净资产收益率	%
	Fsr	第一控股股东持股比例	%
	Lev	负债总额/资产总额	%
	Own	当企业控股股东为国家或地方政府时取值为1;否则取值为0	—
	Age	计算年-成立年,并取自然对数	—
	Year	年度虚拟变量。共有10个年度,设置9个虚拟变量。	—

编号	单位	平均值	标准差	极小值	极大值
x₁	亿元	13.921	17.320	0.427	91.276
x₂	亿元	32.000	40.302	1.875	183.987
x₃	人	4691	7406	263	71244
x₄	亿元	0.998	1.619	0.001	11.397
y	亿元	40.489	47.480	2.522	239.158
W	亿元	0.339	0.478	0.000	2.949

变量类型	符号	单位	平均值	标准差	极小值	极大值
因变量	GE	—	1.301	0.741	0.260	6.757
自变量	Sub	%	1.175	1.821	0.007	15.338
自变量	Size	—	22.014	1.092	19.212	24.031
控制变量	Roe	%	7.050	13.986	-54.110	84.670
	Fsr	%	34.885	11.943	14.040	91.820
	Lev	%	37.015	20.021	1.561	85.492
	Own	—	0.539	0.498	0.000	1.000
	Age	—	2.862	0.297	2.079	3.784

变量	（1）	（2）	（3）	（4）	（5）
Sub_-1		4.411*** （2.72）	9.484*** （2.92）		4.472* （1.83）
（Sub_-1）²			-48.251* （-1.66）		-15.869* （-1.72）
Size				0.324*** （7.22）	0.296*** （7.19）
Roe	-0.531** （-2.29）	-0.261 （-1.37）	-0.256 （-1.38）	0.158 （0.87）	0.211 （1.21）
Fsr	0.539** （2.31）	0.495* （1.71）	0.491* （1.71）	0.246* （1.88）	0.184* （1.74）
Lev	-0.686*** （-4.73）	-0.699*** （-3.54）	-0.651*** （-3.35）	-0.005*** （-3.93）	-0.076*** （-4.44）
Own	0.015 (0.16)	0.190 (1.25)	0.209 (1.40)	0.018 （0.24）	0.019 （0.27）
Age	-0.204*** (-3.17)	-0.153* （-1.73）	-0.161* （-1.85）	-0.323** (-2.35)	-0.344** (-2.51)
常数项	2.462*** （7.93）	0.824 （1.59）	0.701 （1.38）	0.789* （2.02）	-0.604 （0.95）
Year	控制	控制	控制	控制	控制
Wald检验	107.15**	51.66***	53.08***	123.39***	111.05***
观测值	193	173	173	193	173

变量	全样本		国有企业	民营企业
变量	(6) GMM	(7) DK	(8) FGLS	(9) FGLS
GE_-1	0.351*** （7.39）
Sub_-1	4.672** （2.56）	3.904** （2.90）	5.587** （2.39）	8.622* （1.87）
（Sub_-1）²	-16.189* （-1.92）	-15.461* （-1.78）	-21.491* （-1.76）	-65.313* （-1.75）
Size	0.238*** （6.20）	0.227*** （5.69）	0.290*** （7.02）	0.440*** （6.66）
Roe	-1.136* （-1.92）	-0.781* （-2.05）	0.546* （1.75）	0.052 （0.17）
Fsr	1.335* （1.72）	1.256* （1.91）	0.026* （1.82）	0.506* （1.78）
Lev	-0.367*** （-2.94）	-0.169*** （-2.79）	-0.571*** （-2.78）	-0.642*** （-2.81）
Own	0.082 （1.01）	—	—	—
Age	-0.544*** (-3.34)	-0.138* （-1.76）	-0.476* （-1.91）	-0.213** (-2.69)
常数项	-4.583* （-1.95）	-1.498 （-0.30）	-6.021* （-2.45）	-3.718* （-1.81）
Year	控制	控制	控制	控制
Sargan检验	0.379	—	—	—
AR（1）检验	0.042**	—	—	—
AR（2）检验	0.218	—	—	—
Wald检验	298.37***	—	94.66***	116.70***
F统计量	—	6961.47***	—	—
组间R-squared	—	0.2317	—	—
观测值	173	173	93	80